Depth imaging of multichannel seismic data generally is viewed as a two-step process whereby an estimated velocity model that describes the large-wavelength characteristics of the subsurface is constructed and then used for depth migration studies to estimate small-wavelength subsurface features in the form of depth images. Such seismic imaging can therefore be considered to start with a velocity model and end with a geological model based on the most satisfactory interpretation of the depth images in view of the constraints imposed by estimated velocity model used. If the depth images are not satisfactorily interpretable, the velocity model can generally be modified to determine if a better depth image can be obtained. Thus, the geologic sensibility of the estimated velocity model and the correctness of the migrated depth image in view of the estimated velocity model define the limits of conventional geologic imaging processes.
Limited quality of migrated depth images can be attributed largely to either data or model-based reasons. Data-based reasons include, for example, the presence of statics, coherent noise such as ground roll, random noise, and ghost arrivals beyond the realm of the physics of migration. Model-based reasons include, for example, an inaccurate estimated velocity model that does not allow seismic energy waves to migrate to the correct spatial location. Velocity building models such as, for example, migration velocity analysis (MVA), have used data attributes including coherency and flatness of reflection events in the common-midpoint/common-image-point (CMP/CIP) domain as a function of velocity to validate the actual velocities themselves. In methods such as MVA, higher coherency and data flatness parameters imply more realistic velocities and goodness of fit to derived the geologic images.
An alternative to the above methods for velocity model building is traveltime inversion (TI), which objectively estimates the velocity models to reflect large-wavelength subsurface features. In TI methods, geological medium properties such as, for example, P-wave velocities, are estimated so that observed traveltimes can be simulated more closely by the derived velocity models. Although TI is typically conducted as a ray-based method, it can be conducted in both two- and three dimensions for interpretation of geologic data at different scales.
Velocity models obtained from TI can be particularly good estimates of the true velocity model for several reasons. First, TI honors the physics of wave propagation. Second, the data collected for TI (i.e., the arrival times of direct and reflected wave events) can be weighted according to the confidence with which they are identified in the data. Hence, noise effects can be reduced. Third, TI can be regularized such that large-scale geologic features are imaged first, followed by the smaller-scale features. Resolution of the estimated velocity models is determined by the inherent uncertainties present in the traveltime picking.
Given an appropriate velocity field (i.e., an estimated velocity model), depth migration studies estimate the spatial location of reflectors that create an observed wavefield. In other words, a depth image is generated based on an estimated velocity model. Depth migration may be formulated as a linearized inversion process. Attempts at merging depth migration and inversion include, for example, an operator-driven simultaneous prestack depth migration (PSDM) and velocity modeling approach. An alternative merged approach includes a migration-based traveltime formulation for automatically determining background velocities using local optimization methods. However, none of the approaches presented hereinabove are capable of concurrently generating both a velocity model and a depth image.
Geological imaging methods wherein a velocity model and a depth image are concurrently generated would offer considerable benefits in diverse endeavors such as, for example, subsurface exploration, environmental related imaging, understanding of geodynamic processes, and geotechnical applications for construction and hazard detection. The similar kinematic behavior of migration and inversion offers the opportunity to effectively couple these two features in concurrently producing velocity models and depth images. In the unified imaging methods disclosed herein, an iterative process is used to concurrently produce velocity models and depth images in generating a geologic image.